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More elegant way to generate adjacency matrix from list of tuples


By : Adam Ušela
Date : October 16 2020, 06:10 AM
I wish this help you My algorithm would be something like this:
Find the maximum vertex id. Call this n. Create an n+1 by n+1 array of zeros. Call this M. For each pair x, y in the input list, set M[x][y] = 1
code :


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Adjacency Matrix of Non-numeric tuples


By : Mahesh Bahute
Date : March 29 2020, 07:55 AM
it helps some times You really just need to get the labels for the rows and columns. From there, it's just a few for loops:
code :
from __future__ import print_function

import itertools

reservoir = {
    ('a', 'b'): 2,
    ('a', 'c'): 3,
    ('b', 'a'): 1,
    ('b', 'c'): 3,
    ('c', 'a'): 1,
    ('c', 'b'): 2,
    ('c', 'd'): 5
}

fields = sorted(list(set(itertools.chain.from_iterable(reservoir))))

print(' ', *fields)

for row in fields:
    print(row, end=' ')

    for column in fields:
        print(reservoir.get((row, column), 0), end=' ')

    print()

igraph generate adjacency matrix from adjacency list


By : user2323077
Date : March 29 2020, 07:55 AM
With these it helps You misunderstood the commands. The command get.adjlist takes as parameter an igraph graph object, and returns a list-type object representation of the graph. You are applying this to a data frame which is not being coerced to an igraph object.
Below is the correct way to construct a igraph graph object using a data frame, and how to get various graph representations of this object.
code :
require(reshape2)
net_list <- melt( net_test, id.vars = "id")
net_list <- net_list[ !is.na(net_list$value), c("id", "value") ]
graph_o <- graph.data.frame(net_list) #This is a proper igraph graph object
#got from a data frame directly

list_rep <- get.adjlist(graph_o) #this now returns an adjacency list 
#representation of your graph
matrix_rep <- get.adjacency(graph_o) #this gives you the adjacency
#matrix as a (sparse) matrix with the row and column names as you want.

Is the complexity of prim's MST algorithm by adjacency matrix same as that of adjacency list with linear search?


By : Bas Vegter
Date : March 29 2020, 07:55 AM
Does that help You're right; there's no point in using a fancy heap data structure for Prim on dense graphs. The defining feature of Prim, though, is not the heap so much as the idea of repeatedly extending a partial MST as cheaply as possible.
The point of an adjacency matrix is for space (no pointer overhead) and architectural reasons (sequential accesses are typically much more efficient than random).

Create adjacency matrix from nearest neighbour search. (convert adjacency list to adjacency matrix) - Matlab


By : shwetaqwe
Date : March 29 2020, 07:55 AM
Hope that helps I have a matrix 2000x5, in the first column the point number, and in columns 2-5 the 4 neighbours (0s if there isnt a neighbour). Is there an efficient way to create an adjacency matrix out of this ? , A quick and simple technique:
code :
adjMat = zeros(size(A,1));
for ind = 1:size(A,1)
    % Flag 1 on each row 'ind' at the indices mentioned in col 2-5
    adjMat(ind, nonzeros(A(ind,2:end))) = 1;
end
A = [1 2 3; 2 0 1; 3 1 4; 4 5 3; 5 4 0]

A =

 1     2     3
 2     0     1
 3     1     4
 4     5     3
 5     4     0
adjMat =

 0     1     1     0     0
 1     0     0     0     0
 1     0     0     1     0
 0     0     1     0     1
 0     0     0     1     0
adjMat(nonzeros(A(ind,2:end)),ind) = 1;

Generate adjacency matrix from a list, where adjacency means equal elements


By : Svitlana Rytkina
Date : March 29 2020, 07:55 AM
will help you Use broadcasted comparison -
code :
np.equal.outer(lst, lst).astype(int) # or convert to float
In [787]: lst = [0, 1, 0, 5, 0, 1]

In [788]: np.equal.outer(lst, lst).astype(int)
Out[788]: 
array([[1, 0, 1, 0, 1, 0],
       [0, 1, 0, 0, 0, 1],
       [1, 0, 1, 0, 1, 0],
       [0, 0, 0, 1, 0, 0],
       [1, 0, 1, 0, 1, 0],
       [0, 1, 0, 0, 0, 1]])
In [793]: a = np.asarray(lst)

In [794]: (a[:,None]==a).astype(int)
Out[794]: 
array([[1, 0, 1, 0, 1, 0],
       [0, 1, 0, 0, 0, 1],
       [1, 0, 1, 0, 1, 0],
       [0, 0, 0, 1, 0, 0],
       [1, 0, 1, 0, 1, 0],
       [0, 1, 0, 0, 0, 1]])
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